Project: Investigate a Dataset (TMDb Movie Dataset)¶

Table of Contents¶

Introduction

Creating a Copy of The Dataset

Making a Copy of The Dataset

Data Cleaning

Exporting the Cleaned Data to a New CSV

Exploratory Data Analysis

Visual Trends

Conclusion

Introduction¶

This dataset contains information about over 10,000 movies collected from the movie database (TMDB), which was collected between 1960 and 2015, with information such as title, budget, revenue, cast, director, genres, release date, release year, runtime, etc. Certain columns, like ‘cast’ and ‘genres’, contain multiple values separated by pipe (|) characters.

The primary goals of the project include data collation and exploratory data analysis . This project uses Python's Pandas library to evaluate and clean data for fast visualization and exploration through the built-in functions of Pandas in data analysis and, finally, by descriptive statistics Matplotlib to visualize the results.

  • Question 1. What are the top 20 movies based on their popularity?
  • Question 2. What are the top 10 movies based on their budget and revenue?
  • Question 3: How do movie budgets and profits compare? Does a high budget equal a high profit?
  • Question 4. What is the distribution of the movies over all the years?
  • Question 5. What is the relationship between the popularity and the runtime of movies?
  • Question 6. What is the average vote count for each year?
  • Question 7. The top 10 actors? What is the movie title, average vote count and the revenue generated?
  • Question 8. Which genre has the highest release of movies?
  • Question 9. Top 5 Directors: Who directs the movies with the highest revenue generated?
  • Question 10. Which Month Released The Highest Number Of Movies In All Of The Years? And which month made the highest average revenue?
In [4]:
#importing the required libraries
import pandas as pd
import numpy as np
import seaborn as sns
import plotly.express as px
import plotly.express as go
import matplotlib.pyplot as plt
In [83]:
#reading through the data
df = pd.read_csv('tmdb-movies.csv')
df.head(3)
Out[83]:
id imdb_id popularity budget revenue original_title cast homepage director tagline ... overview runtime genres production_companies release_date vote_count vote_average release_year budget_adj revenue_adj
0 135397 tt0369610 32.985763 150000000 1513528810 Jurassic World Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi... http://www.jurassicworld.com/ Colin Trevorrow The park is open. ... Twenty-two years after the events of Jurassic ... 124 Action|Adventure|Science Fiction|Thriller Universal Studios|Amblin Entertainment|Legenda... 6/9/15 5562 6.5 2015 1.379999e+08 1.392446e+09
1 76341 tt1392190 28.419936 150000000 378436354 Mad Max: Fury Road Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic... http://www.madmaxmovie.com/ George Miller What a Lovely Day. ... An apocalyptic story set in the furthest reach... 120 Action|Adventure|Science Fiction|Thriller Village Roadshow Pictures|Kennedy Miller Produ... 5/13/15 6185 7.1 2015 1.379999e+08 3.481613e+08
2 262500 tt2908446 13.112507 110000000 295238201 Insurgent Shailene Woodley|Theo James|Kate Winslet|Ansel... http://www.thedivergentseries.movie/#insurgent Robert Schwentke One Choice Can Destroy You ... Beatrice Prior must confront her inner demons ... 119 Adventure|Science Fiction|Thriller Summit Entertainment|Mandeville Films|Red Wago... 3/18/15 2480 6.3 2015 1.012000e+08 2.716190e+08

3 rows × 21 columns

In [6]:
#information about the data
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10866 entries, 0 to 10865
Data columns (total 21 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   id                    10866 non-null  int64  
 1   imdb_id               10856 non-null  object 
 2   popularity            10866 non-null  float64
 3   budget                10866 non-null  int64  
 4   revenue               10866 non-null  int64  
 5   original_title        10866 non-null  object 
 6   cast                  10790 non-null  object 
 7   homepage              2936 non-null   object 
 8   director              10822 non-null  object 
 9   tagline               8042 non-null   object 
 10  keywords              9373 non-null   object 
 11  overview              10862 non-null  object 
 12  runtime               10866 non-null  int64  
 13  genres                10843 non-null  object 
 14  production_companies  9836 non-null   object 
 15  release_date          10866 non-null  object 
 16  vote_count            10866 non-null  int64  
 17  vote_average          10866 non-null  float64
 18  release_year          10866 non-null  int64  
 19  budget_adj            10866 non-null  float64
 20  revenue_adj           10866 non-null  float64
dtypes: float64(4), int64(6), object(11)
memory usage: 1.7+ MB
In [7]:
#decription of the data
df.describe()
Out[7]:
id popularity budget revenue runtime vote_count vote_average release_year budget_adj revenue_adj
count 10866.000000 10866.000000 1.086600e+04 1.086600e+04 10866.000000 10866.000000 10866.000000 10866.000000 1.086600e+04 1.086600e+04
mean 66064.177434 0.646441 1.462570e+07 3.982332e+07 102.070863 217.389748 5.974922 2001.322658 1.755104e+07 5.136436e+07
std 92130.136561 1.000185 3.091321e+07 1.170035e+08 31.381405 575.619058 0.935142 12.812941 3.430616e+07 1.446325e+08
min 5.000000 0.000065 0.000000e+00 0.000000e+00 0.000000 10.000000 1.500000 1960.000000 0.000000e+00 0.000000e+00
25% 10596.250000 0.207583 0.000000e+00 0.000000e+00 90.000000 17.000000 5.400000 1995.000000 0.000000e+00 0.000000e+00
50% 20669.000000 0.383856 0.000000e+00 0.000000e+00 99.000000 38.000000 6.000000 2006.000000 0.000000e+00 0.000000e+00
75% 75610.000000 0.713817 1.500000e+07 2.400000e+07 111.000000 145.750000 6.600000 2011.000000 2.085325e+07 3.369710e+07
max 417859.000000 32.985763 4.250000e+08 2.781506e+09 900.000000 9767.000000 9.200000 2015.000000 4.250000e+08 2.827124e+09
In [8]:
#columns with null values
df.isnull().sum()
Out[8]:
id                         0
imdb_id                   10
popularity                 0
budget                     0
revenue                    0
original_title             0
cast                      76
homepage                7930
director                  44
tagline                 2824
keywords                1493
overview                   4
runtime                    0
genres                    23
production_companies    1030
release_date               0
vote_count                 0
vote_average               0
release_year               0
budget_adj                 0
revenue_adj                0
dtype: int64
In [9]:
#the number of rows and columns 
df.shape
Out[9]:
(10866, 21)
In [10]:
#counts of duplicated values in each columns
df.duplicated().value_counts()
Out[10]:
False    10865
True         1
dtype: int64
In [11]:
#the unique values in twitter enhanced archive csv information
df.nunique()
Out[11]:
id                      10865
imdb_id                 10855
popularity              10814
budget                    557
revenue                  4702
original_title          10571
cast                    10719
homepage                 2896
director                 5067
tagline                  7997
keywords                 8804
overview                10847
runtime                   247
genres                   2039
production_companies     7445
release_date             5909
vote_count               1289
vote_average               72
release_year               56
budget_adj               2614
revenue_adj              4840
dtype: int64

Creating a Copy of The Dataset¶

In [12]:
# creating a copy of the dataset
df_m = df.copy()

Data Cleaning¶

Issue 1: Replacing the cast null values with None and seperating their details to get the exact cast details for each movie¶

In [13]:
#function to replace the slashes column and null values with None
def column__replace(column):
    column = str(column).replace("|",",")
    column = column.replace('nan', 'None')
    return column
In [14]:
#replacing using the replace function
df_m['cast'] = df_m['cast'].apply(column__replace)

Issue 2: Replacing the keywords null values with None and seperating their details to get the exact keywords¶

In [15]:
#replacing using the replace function
df_m['keywords'] = df_m['keywords'].apply(column__replace)

Issue 3: Replacing the genres null values with None and seperating their details to get the exact genres¶

In [16]:
#replacing using the replace function
df_m['genres'] = df_m['genres'].apply(column__replace)

Issue 4: Replacing the production_companies null values with None and seperating their details to get the exact production_companies¶

In [17]:
#replacing using the replace function
df_m['production_companies'] = df_m['production_companies'].apply(column__replace)

Issue 5: Replacing the directors Nan values with None¶

In [18]:
df_m['director'] = df_m['director'].fillna('None')

Issue 6: Normalizing the release date to extract the release week day and release month¶

In [19]:
#to extract the weekdays
df_m['release_date_of_week'] = pd.to_datetime(df_m.release_date).dt.day_name()

#to extract the month
df_m['month'] = pd.to_datetime(df_m.release_date).dt.strftime('%b')

Issue 7: Extracting the profit generated from the budget and revenue¶

In [20]:
#i would be using the budget and revenue adjustment, this is because the initial budget and revenue is likely to be proposed  and not implemented
df_m['profit'] = df_m['revenue_adj']- df_m['budget_adj']

Issue 8: Creating a range for the release year¶

In [21]:
#categorising release year to different year ranges
bins = [-1, 1960, 1965, 1970, 1980, 1990, 2000, np.inf]
names = ['<1960','1960-1970', '1970-1980','1980-1990', '1990-2000', '2000-2010', '2010-2020']
        
#grouping year into different year groups 
df_m['release_year_ranges'] = pd.cut(df_m['release_year'], bins, labels = names)

#Creating an index for release year groups to enable in an ascending order
movies_year_index = ['<1960','1960-1970', '1970-1980','1980-1990', '1990-2000', '2000-2010', '2010-2020']

# Ordering the index
df_m['release_year_ranges'] = pd.Categorical(df_m['release_year_ranges'], 
                                                  categories = movies_year_index, ordered=True)

Issue 9: Extracting the actors details¶

In [25]:
#selecting the first cast in each movie as the main actor
df_m['actor'] = df_m['cast'].apply(lambda x: x.split(',')[0])

Issue 10: Extracting the actors details¶

In [26]:
#selecting the first genre in each movie as the main genre
df_m['main_genre'] = df_m['genres'].apply(lambda x: x.split(',')[0])
In [27]:
df_m.head(2)
Out[27]:
id imdb_id popularity budget revenue original_title cast homepage director tagline ... vote_average release_year budget_adj revenue_adj release_date_of_week month profit release_year_ranges actor main_genre
0 135397 tt0369610 32.985763 150000000 1513528810 Jurassic World Chris Pratt,Bryce Dallas Howard,Irrfan Khan,Vi... http://www.jurassicworld.com/ Colin Trevorrow The park is open. ... 6.5 2015 1.379999e+08 1.392446e+09 Tuesday Jun 1.254446e+09 2010-2020 Chris Pratt Action
1 76341 tt1392190 28.419936 150000000 378436354 Mad Max: Fury Road Tom Hardy,Charlize Theron,Hugh Keays-Byrne,Nic... http://www.madmaxmovie.com/ George Miller What a Lovely Day. ... 7.1 2015 1.379999e+08 3.481613e+08 Wednesday May 2.101614e+08 2010-2020 Tom Hardy Action

2 rows × 27 columns

Dropping Columns¶

Unnecessary would be dropped off.

In [28]:
#dropping the columns
df_m.drop(['id','imdb_id', 'budget', 'revenue', 'homepage', 'tagline', 'overview'], axis = 1, inplace = True)

Drop Duplicates¶

This dataset has rows movie that are duplicates, therefore, these would be dropped.

In [29]:
#finding out number of duplicates rows
df_m.duplicated().value_counts()
Out[29]:
False    10865
True         1
dtype: int64
In [30]:
#locating the duplicate rows
df2 = df_m.loc[df_m['popularity']== 0.59643]
df2
Out[30]:
popularity original_title cast director keywords runtime genres production_companies release_date vote_count vote_average release_year budget_adj revenue_adj release_date_of_week month profit release_year_ranges actor main_genre
2089 0.59643 TEKKEN Jon Foo,Kelly Overton,Cary-Hiroyuki Tagawa,Ian... Dwight H. Little martial arts,dystopia,based on video game,mart... 92 Crime,Drama,Action,Thriller,Science Fiction Namco,Light Song Films 3/20/10 110 5.0 2010 30000000.0 967000.0 Saturday Mar -29033000.0 2010-2020 Jon Foo Crime
2090 0.59643 TEKKEN Jon Foo,Kelly Overton,Cary-Hiroyuki Tagawa,Ian... Dwight H. Little martial arts,dystopia,based on video game,mart... 92 Crime,Drama,Action,Thriller,Science Fiction Namco,Light Song Films 3/20/10 110 5.0 2010 30000000.0 967000.0 Saturday Mar -29033000.0 2010-2020 Jon Foo Crime
In [31]:
#dropping duplicates
df_m.drop_duplicates(inplace=True)

Exporting the Cleaned Data to a New CSV¶

In [32]:
#exporting the data to a new csv file
df_movies = df_m.to_csv('cleaned_tmdb-movies.csv', index = False)

Exploratory Data Analysis¶

In [33]:
df_movies = pd.read_csv('cleaned_tmdb-movies.csv')
df_movies.head(2)
Out[33]:
popularity original_title cast director keywords runtime genres production_companies release_date vote_count vote_average release_year budget_adj revenue_adj release_date_of_week month profit release_year_ranges actor main_genre
0 32.985763 Jurassic World Chris Pratt,Bryce Dallas Howard,Irrfan Khan,Vi... Colin Trevorrow monster,dna,tyrannosaurus rex,velociraptor,island 124 Action,Adventure,Science Fiction,Thriller Universal Studios,Amblin Entertainment,Legenda... 6/9/15 5562 6.5 2015 1.379999e+08 1.392446e+09 Tuesday Jun 1.254446e+09 2010-2020 Chris Pratt Action
1 28.419936 Mad Max: Fury Road Tom Hardy,Charlize Theron,Hugh Keays-Byrne,Nic... George Miller future,chase,post-apocalyptic,dystopia,australia 120 Action,Adventure,Science Fiction,Thriller Village Roadshow Pictures,Kennedy Miller Produ... 5/13/15 6185 7.1 2015 1.379999e+08 3.481613e+08 Wednesday May 2.101614e+08 2010-2020 Tom Hardy Action

Visual Trends¶

In [34]:
df_movies.hist(figsize=(10,9), density=True, color='#083AA9')
#showing the chart
plt.show()

Question 1. What are the top 20 movies based on their popularity?

In [35]:
#ordering the movies in descending order
df_movies_t = df_movies.groupby('original_title')['popularity'].sum()
df_movies_tt= df_movies_t.sort_values(ascending=False).reset_index()
df_movies_title = df_movies_tt.head(20)
df_movies_title
Out[35]:
original_title popularity
0 Jurassic World 32.985763
1 Mad Max: Fury Road 28.419936
2 Interstellar 24.949134
3 Guardians of the Galaxy 14.311205
4 Insurgent 13.112507
5 Captain America: The Winter Soldier 12.971027
6 Star Wars 12.037933
7 John Wick 11.422751
8 Star Wars: The Force Awakens 11.173104
9 The Hunger Games: Mockingjay - Part 1 10.739009
10 The Hobbit: The Battle of the Five Armies 10.174599
11 Avatar 9.432768
12 The Revenant 9.421300
13 Inception 9.363643
14 Furious 7 9.335014
15 Fight Club 8.947905
16 Big Hero 6 8.691294
17 Terminator Genisys 8.654359
18 The Lord of the Rings: The Fellowship of the Ring 8.575419
19 The Dark Knight 8.466668
In [36]:
#plotting the chart to display the analysis
plt.figure(figsize=(20,8))
ax = sns.barplot(data =df_movies_title, y='original_title', x='popularity', palette='winter_r',  dodge=False)

#setting the properties for the chart
sns.set_theme(style="darkgrid")
ax.grid(False)
ax.set_title('top 20 movies based on their popularity', fontsize =20)
ax.set_xlabel('movie title', fontsize = 15)
ax.set_ylabel('total popularity', fontsize = 15)

#showing the chart
plt.show()
  • From the chart above, it can be seen that Jurassic world movie has the highest popularity follwed by Mad Max etc

Question 2. What are the top 10 movies based on their budget and revenue?

In [37]:
#grouping the movies in descending order of their budget
movies_b = df_movies.groupby('original_title')['budget_adj'].sum()
movies_bdgt = movies_b.sort_values(ascending=False)

#getting the top budget movies
movies_budgt = movies_bdgt.head(10)
movies_budget = movies_budgt.reset_index()
In [38]:
#grouping the movies in descending order of their revenue
movies_r = df_movies.groupby('original_title')['revenue_adj'].sum()
movies_rev = movies_r.sort_values(ascending=False)

#getting the top revenue movies
movies_revne = movies_rev.head(10)
movies_revenue = movies_revne.reset_index()
In [39]:
#plotting the chart
plt.figure(figsize=(12,8))
ax = sns.barplot(data = movies_budget, x='original_title', y='budget_adj', color='#083AA9',  edgecolor="#409240", linewidth = 2)
plt.title('top 10 movies based on their budget')
plt.ylabel('budget')
ax.grid(False)
plt.xlabel('movie titles')
plt.xticks(rotation=30, ha='right')
#showing the chart
plt.show()

#plotting the chart
plt.figure(figsize=(12,8))
ax = sns.barplot(data = movies_revenue , x='original_title', y= 'revenue_adj', color='#1972A4',  edgecolor="blue", linewidth = 2)
plt.title('top 10 movies based on their revenue')
ax.grid(False)
plt.ylabel('revenue')
plt.xlabel('movie titles')
plt.xticks(rotation=30, ha='right')

#showing the chart
plt.show()

The chart above indicates that Avatar by James Cameron had the highest revenue generated even though the warriors way had the highest budget

Question 3: How do movies budgets and profits compare? Does a high budget equal a high profit?

In [40]:
fig = px.scatter(df_movies ,x = 'profit', y ='budget_adj', trendline = 'ols')
fig.update_layout(title_text ='budget and profit relationship', xaxis_title = 'profit',
                  yaxis_title = 'budget')

#setting the chart properties
fig.update_layout(
    autosize=False,
    width=1000,
    height=700,)

#hiding the gridlines
fig.update_xaxes(showgrid = False)
fig.update_yaxes(showgrid = False)

fig.show()

A positive correlation exists between a decent budget which in turn generates a good profit. Profit and budget have a positive correlation, so there is a good chance that movies with higher investments will result in higher profits.

Question 4. What is the distribution of the movies over all the years?

In [58]:
#grouping the movies dist based on year, week and count of movies released
movies_d  = df_movies.groupby(['release_year_ranges', 'release_date_of_week'])['original_title'].count().reset_index()
movies_dist = movies_d.sort_values(['release_year_ranges', 'release_date_of_week'],ascending=False)
movies_dist.head(10)
Out[58]:
release_year_ranges release_date_of_week original_title
47 <1960 Wednesday 4
46 <1960 Tuesday 9
45 <1960 Thursday 8
44 <1960 Sunday 3
43 <1960 Saturday 5
42 <1960 Monday 3
41 2010-2020 Wednesday 940
40 2010-2020 Tuesday 846
39 2010-2020 Thursday 1176
38 2010-2020 Sunday 484
In [59]:
#grouping the movies for comparison with earlier result in descending order of their budget
movies_d = df_movies.groupby('release_year_ranges')['original_title'].count()
movies_dist1 = movies_d.sort_values(ascending=False).reset_index()
movies_dist1
Out[59]:
release_year_ranges original_title
0 2010-2020 6940
1 2000-2010 1860
2 1990-2000 1117
3 1980-1990 545
4 1970-1980 197
5 1960-1970 174
6 <1960 32
In [60]:
#plotting the chart
plt.figure(figsize=(20,8))
fig = px.line(movies_dist, x = 'release_year_ranges', y = 'original_title', color="release_date_of_week")
fig.update_traces(line=dict( width=3.0))

fig.update_layout(title_text ='weekly distribution of movies over the years', xaxis_title = 'year ranges',
                  yaxis_title = 'movies count')
#setting the chart properties
fig.update_layout(
    autosize=False,
    width=1100,
    height=600,)

#showing the chart
fig.show()

<Figure size 1440x576 with 0 Axes>

The maximum number of movies released is between the years 2010-2020, with the majority of them being released on weekdays(Thursdays and Fridays).

Question 5. What is the relationship between the popularity and the runtime of movies?

In [62]:
#plotting the results
plt.figure(figsize=(15,8))
sns.set_style('whitegrid')

ax = sns.scatterplot(y = 'popularity', x = 'runtime', data = df_movies, color='#425F57')
ax.grid(False)
# setting the properties for the chart
plt.title('the relationship between popularity and runtime of movies', fontsize = 15) 
plt.xlabel('runtime')
plt.ylabel('popularity')

#saving the chart
plt.show()

According to the plot, we can say that movies in the range of 100–200 runtime are more popular, while movies above the runtime of 200 minutes generally have decreased popularity.  This is because long-running movies get boring and unexciting.

Question 6. What is the average vote count for each year?

In [75]:
#plotting the chart
fig = plt.figure(figsize=(8,8))
ax = sns.boxplot(y='vote_average', x='release_year_ranges',  data = df_movies, palette ='viridis')
plt.ylim(0,10)
plt.title('the average ratings of movies per year', fontsize = 15)
plt.show()

Question 7. The top 10 actors? What is the movie title, average vote count and the revenue generated?

In [79]:
#First calculate the total number of movies by each actor, their revenue, and the average rating average
movies_a = df_movies.groupby('actor')['original_title'].count().reset_index()
movies_r = df_movies.groupby('actor')['profit'].sum().reset_index()
movies_v = round(df_movies.groupby('actor')['vote_average'].mean().reset_index(),2)
In [80]:
# Coupled in a table all
all = movies_a.merge(movies_r, on='actor', how='inner')
all = all.merge(movies_v, on='actor', how='inner')
all
Out[80]:
actor original_title profit vote_average
0 "Weird Al" Yankovic 1 -8.794925e+06 6.70
1 50 Cent 6 -3.264166e+07 5.57
2 A. Michael Baldwin 1 -3.677779e+06 5.50
3 A.J. Cook 1 0.000000e+00 5.40
4 AJ Michalka 1 0.000000e+00 6.10
... ... ... ... ...
4296 Ólafur Darri Ólafsson 1 -2.849233e+06 6.20
4297 Óscar Jaenada 1 0.000000e+00 5.30
4298 Çetin Tekindor 1 0.000000e+00 8.20
4299 Émilie Dequenne 1 0.000000e+00 6.00
4300 Àlex Brendemühl 1 0.000000e+00 5.90

4301 rows × 4 columns

In [81]:
top_10_actors = all.sort_values(by='profit', ascending=False)[:10].reset_index()         
top_10_actors
Out[81]:
index actor original_title profit vote_average
0 4059 Tom Hanks 37 7.717645e+09 6.60
1 4052 Tom Cruise 31 7.376906e+09 6.35
2 937 Daniel Radcliffe 14 7.076231e+09 6.94
3 1551 Harrison Ford 30 6.600949e+09 6.19
4 2716 Mark Hamill 4 5.310129e+09 7.62
5 3708 Sean Connery 29 5.126340e+09 6.24
6 3495 Robert Downey Jr. 17 4.733728e+09 6.51
7 3913 Sylvester Stallone 37 4.480025e+09 5.87
8 3651 Sandra Bullock 24 4.367335e+09 5.91
9 4227 Will Smith 16 4.210883e+09 6.49
In [82]:
sns.set_style("whitegrid")
#make a subplot of size 1,2

plt.figure(figsize=(20,20))
fig.tight_layout(pad=3)

#actors revenue generated plot
plt.subplot(2,2,1)
ax1 = sns.barplot(data = top_10_actors, x='actor', y='profit', color='#256D85',  edgecolor="#CFFF8D", linewidth = 2)
ax1.title.set_text('profit generated by top 10 actor')
plt.xticks(rotation=30, ha='right')

#actors average voters plot
plt.subplot(2,2,2)
ax2 = sns.barplot(data = top_10_actors, x='actor', y='vote_average', color='#256D85',  edgecolor="#CFFF8D", linewidth = 2)
ax2.title.set_text('average vote generated by top 10 actor')
plt.xticks(rotation=30, ha='right')
plt.show()

From the charts above, we can see that in terms of profit, Tom Hanks's movies generated a lot of profit and one of the highest count of movies released and fairly high ratings, while Sylvester Stallone also has the highest movie count and Mark Hamill has the highest average ratings.

Question 8. Which genre has the highest release of movies?

In [68]:
# count plot on single categorical variable
plt.figure(figsize = (20,8))
plt.xticks(rotation = 30, ha='right')
ax = sns.countplot(x ='main_genre', data = df_movies,  palette ='mako')
ax.grid(False)
plt.show()

According to the above analysis, drama is the most popular genre when compared to other categories, followed by comedy, action, and horror.

Question 9. Top 5 Directors: Who directs the movies with the highest revenue generated?

In [69]:
top_directors = df_movies.groupby('director')['revenue_adj'].sum().reset_index().sort_values(['revenue_adj'], 
            ascending=False)[:5]
top_directors
Out[69]:
director revenue_adj
4602 Steven Spielberg 1.520245e+10
1997 James Cameron 7.327221e+09
3801 Peter Jackson 7.019848e+09
1679 George Lucas 6.313919e+09
4141 Robert Zemeckis 5.655648e+09
In [70]:
# fig = plt.gcf()
# fig.set_size_inches(8,8)

# fig = plt.gcf()
# fig.set_size_inches(7,7)
# plt.figure(figsize = (20,8))
explode = [0, 0,0.1,0,0]
top_directors.groupby(['director']).sum().plot(kind='pie', y='revenue_adj', autopct='%.2f %%', 
                    colors = ['#C1EFFF', '#0096FF', '#008DB8', '#E2DCC8', '#FFB3B3'],
                    title = 'top 5 directors', 
                    explode = explode, fontsize=10, shadow = True, 
                    subplots='true',
                    figsize=(9,7))

#setting the properties for the chart
plt.axis('equal')
plt.legend(loc = 'upper left')

#saving the chart
plt.show()

Steven Spielberg directed the most popular movie, Jurassic World and a couple of other movies, so it's kind of expected to be the director with the highest revenue generated.

In [71]:
df_movies.head(2)
Out[71]:
popularity original_title cast director keywords runtime genres production_companies release_date vote_count vote_average release_year budget_adj revenue_adj release_date_of_week month profit release_year_ranges actor main_genre
0 32.985763 Jurassic World Chris Pratt,Bryce Dallas Howard,Irrfan Khan,Vi... Colin Trevorrow monster,dna,tyrannosaurus rex,velociraptor,island 124 Action,Adventure,Science Fiction,Thriller Universal Studios,Amblin Entertainment,Legenda... 6/9/15 5562 6.5 2015 1.379999e+08 1.392446e+09 Tuesday Jun 1.254446e+09 2010-2020 Chris Pratt Action
1 28.419936 Mad Max: Fury Road Tom Hardy,Charlize Theron,Hugh Keays-Byrne,Nic... George Miller future,chase,post-apocalyptic,dystopia,australia 120 Action,Adventure,Science Fiction,Thriller Village Roadshow Pictures,Kennedy Miller Produ... 5/13/15 6185 7.1 2015 1.379999e+08 3.481613e+08 Wednesday May 2.101614e+08 2010-2020 Tom Hardy Action

Question 10. Which Month Released The Highest Number Of Movies In All Of The Years? And which month made the highest average revenue?

In [72]:
# count plot on single categorical variable
plt.figure(figsize = (20,8))
plt.xticks(rotation = 30, ha='right')
ax = sns.countplot(x ='month', data = df_movies,  palette ='YlGnBu')
ax.grid(False)
plt.show()

From the plot trend, we can conclude that there are a higher number of movies released in September and in October.

Conclusion¶

From the overall analysis, certain conclusions have been drawn, which include the following

  • Short duration movies are more popular than long duration movies.

  • Movies with high revenue have shown a corresponding increase in the profit. There is a high probability that movies with higher investments result in better profit.

  • September, October and December are most popular month for movies released.

  • Drama is the most popular genre, followed by action, comedy and horror.

  • Drama, Comedy,Action and Horror are top genres in this dataset.

  • The most bankable star that can generate the highest total profit is Tom Hanks. The bankable actor will attract people to go to see movies.

  • According to TMDB dataset, all times most popular movies are Jurassic World, Mad Max: Fury Road, and Interstellar.

  • The most filmed director was Steven Spielberg, James Cameron, and Peter Jackson.

In [ ]: